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Australian Public Preferences for the Funding of New Health Technologies

Author

Listed:
  • Jennifer A. Whitty
  • Julie Ratcliffe
  • Gang Chen
  • Paul A. Scuffham

Abstract

Background. Ethical, economic, political, and legitimacy arguments support the consideration of public preferences in health technology decision making. The objective was to assess public preferences for funding new health technologies and to compare a profile case best-worst scaling (BWS) and traditional discrete choice experiment (DCE) method. Methods. An online survey consisting of a DCE and BWS task was completed by 930 adults recruited via an Internet panel. Respondents traded between 7 technology attributes. Participation quotas broadly reflected the population of Queensland, Australia, by gender and age. Choice data were analyzed using a generalized multinomial logit model. Results. The findings from both the BWS and DCE were generally consistent in that respondents exhibited stronger preferences for technologies offering prevention or early diagnosis over other benefit types. Respondents also prioritized technologies that benefit younger people, larger numbers of people, those in rural areas, or indigenous Australians; that provide value for money; that have no available alternative; or that upgrade an existing technology. However, the relative preference weights and consequent preference orderings differed between the DCE and BWS models. Further, poor correlation between the DCE and BWS weights was observed. While only a minority of respondents reported difficulty completing either task (22.2% DCE, 31.9% BWS), the majority (72.6%) preferred the DCE over BWS task. Conclusions. This study provides reassurance that many criteria routinely used for technology decision making are considered to be relevant by the public. The findings clearly indicate the perceived importance of prevention and early diagnosis. The dissimilarity observed between DCE and profile case BWS weights is contrary to the findings of previous comparisons and raises uncertainty regarding the comparative merits of these stated preference methods in a priority-setting context.

Suggested Citation

  • Jennifer A. Whitty & Julie Ratcliffe & Gang Chen & Paul A. Scuffham, 2014. "Australian Public Preferences for the Funding of New Health Technologies," Medical Decision Making, , vol. 34(5), pages 638-654, July.
    • Handle: RePEc:sae:medema:v:34:y:2014:i:5:p:638-654
    DOI: 10.1177/0272989X14526640
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    References listed on IDEAS

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    4. Qinxin Guo & Junyi Shen, 2019. "An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice," Discussion Paper Series DP2019-14, Research Institute for Economics & Business Administration, Kobe University.
    5. Tatenda T Yemeke & Elizabeth E Kiracho & Aloysius Mutebi & Rebecca R Apolot & Anthony Ssebagereka & Daniel R Evans & Sachiko Ozawa, 2020. "Health versus other sectors: Multisectoral resource allocation preferences in Mukono district, Uganda," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    6. Soto, José R. & Adams, Damian C. & Escobedo, Francisco J., 2016. "Landowner attitudes and willingness to accept compensation from forest carbon offsets: Application of best–worst choice modeling in Florida USA," Forest Policy and Economics, Elsevier, vol. 63(C), pages 35-42.
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    8. Lesley Chim & Glenn Salkeld & Patrick Kelly & Wendy Lipworth & Dyfrig A Hughes & Martin R Stockler, 2017. "Societal perspective on access to publicly subsidised medicines: A cross sectional survey of 3080 adults in Australia," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-24, March.
    9. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    10. Marta Trapero-Bertran & Beatriz Rodríguez-Martín & Julio López-Bastida, 2019. "What attributes should be included in a discrete choice experiment related to health technologies? A systematic literature review," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
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    12. Gu, Yuanyuan & Lancsar, Emily & Ghijben, Peter & Butler, James RG & Donaldson, Cam, 2015. "Attributes and weights in health care priority setting: A systematic review of what counts and to what extent," Social Science & Medicine, Elsevier, vol. 146(C), pages 41-52.

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